Singing wet and dry: Exploring alcohol regulation through music, 1885–1919
Bibliographic record
Abstract
Despite abundant research on the topic of temperance and prohibition in North America, very little has been written about the relationship between music and alcohol \nregulation during the late-nineteenth and early-twentieth centuries. Both pro-drink (wet) cultures and anti-drink (dry) cultures amassed several hundred songs in support of their cause. This study compares these songs within the geographical context of Canada and northern North America during the years leading up to prohibition. It assesses both wet and dry songs’ relative success at attaching their causes to hegemonic ideologies, social \ngroups, technologies, and modes of organization. It concludes that, during the period in question, dry music was more adept in each of these respects. This study contributes to current scholarship by demonstrating that wet and dry cultures in North America cannot be completely understood without also studying their music.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".